Secure Multilayer Perceptron Based on Homomorphic Encryption

  • Reda BellafqiraEmail author
  • Gouenou Coatrieux
  • Emmanuelle Genin
  • Michel Cozic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11378)


In this work, we propose an outsourced Secure Multilayer Perceptron (SMLP) scheme where privacy and confidentiality of the data and the model are ensured during its training and the classification phases. More clearly, this SMLP: (i) can be trained by a cloud server based on data previously outsourced by a user in an homomorphically encrypted form; its parameters are homomorphically encrypted giving thus no clues about them to the cloud; and (ii) can also be used for classifying new encrypted data sent by the user while returning him the encrypted classification result. The originality of this scheme is threefold: To the best of our knowledge, it is the first multilayer perceptron (MLP) secured homomorphically in its training phase with no problem of convergence. It does not require extra-communications with the user. And, is based on the Rectified Linear Unit (ReLU) activation function that we secure with no approximation contrarily to actual SMLP solutions. To do so, we take advantage of two semi-honest non-colluding servers. Experimental results carried out on a binary database encrypted with the Paillier cryptosystem demonstrate the overall performance of our scheme and its convergence.


Secure neural network Multilayer perceptron Homomorphic encryption Cloud computing 


  1. 1.
    Aono, Y., Hayashi, T., Wang, L., Moriai, S., et al.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 13(5), 1333–1345 (2018)CrossRefGoogle Scholar
  2. 2.
    Bellafqira, R., Coatrieux, G., Bouslimi, D., Quellec, G.: Content-based image retrieval in homomorphic encryption domain. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2944–2947. IEEE (2015)Google Scholar
  3. 3.
    Bellafqira, R., Coatrieux, G., Bouslimi, D., Quellec, G.: An end to end secure CBIR over encrypted medical database. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 2537–2540. IEEE (2016)Google Scholar
  4. 4.
    Bellafqira, R., Coatrieux, G., Bouslimi, D., Quellec, G., Cozic, M.: Proxy re-encryption based on homomorphic encryption. In: Proceedings of the 33rd Annual Computer Security Applications Conference, pp. 154–161. ACM (2017)Google Scholar
  5. 5.
    Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: NDSS (2015)Google Scholar
  6. 6.
    Bouslimi, D., Bellafqira, R., Coatrieux, G.: Data hiding in homomorphically encrypted medical images for verifying their reliability in both encrypted and spatial domains. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 2496–2499. IEEE (2016)Google Scholar
  7. 7.
    Castellano, G., Fanelli, A.M.: Variable selection using neural-network models. Neurocomputing 31(1), 1–13 (2000)CrossRefGoogle Scholar
  8. 8.
    Chabanne, H., de Wargny, A., Milgram, J., Morel, C., Prouff, E.: Privacy-preserving classification on deep neural network. IACR Cryptol. ePrint Archive 2017, 35 (2017)Google Scholar
  9. 9.
    Decencière, E., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)CrossRefGoogle Scholar
  10. 10.
    Ding, W., Yan, Z., Deng, R.H.: Encrypted data processing with homomorphic re-encryption. Inf. Sci. 409, 35–55 (2017)CrossRefGoogle Scholar
  11. 11.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)Google Scholar
  12. 12.
    Goldreich, O.: Foundations of Cryptography. Basic Applications, vol. 2. Cambridge University Press, New York (2009)Google Scholar
  13. 13.
    Hsu, C.Y., Lu, C.S., Pei, S.C.: Image feature extraction in encrypted domain with privacy-preserving sift. IEEE Trans. Image Process. 21(11), 4593–4607 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Huang, Y., Evans, D., Katz, J., Malka, L.: Faster secure two-party computation using garbled circuits. In: USENIX Security Symposium, vol. 201 (2011)Google Scholar
  15. 15.
    Kerschbaum, F., Biswas, D., de Hoogh, S.: Performance comparison of secure comparison protocols. In: 20th International Workshop on Database and Expert Systems Application, pp. 133–136. IEEE (2009)Google Scholar
  16. 16.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). Scholar
  17. 17.
    Schlitter, N.: A protocol for privacy preserving neural network learning on horizontal partitioned data. In: PSD (2008)Google Scholar
  18. 18.
    Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp. 1310–1321. ACM (2015)Google Scholar
  19. 19.
    Veugen, T.: Encrypted integer division. In: 2010 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2010)Google Scholar
  20. 20.
    Wu, F., Zhong, H., Shi, R., Huang, H.: Secure two-party computation of the quadratic function’s extreme minimal value. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2975–2978. IEEE (2012)Google Scholar
  21. 21.
    Xie, P., Bilenko, M., Finley, T., Gilad-Bachrach, R., Lauter, K., Naehrig, M.: Crypto-nets: Neural networks over encrypted data. arXiv preprint arXiv:1412.6181 (2014)
  22. 22.
    Zheng, S., et al.: Asynchronous stochastic gradient descent with delay compensation. arXiv preprint arXiv:1609.08326 (2016)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Reda Bellafqira
    • 1
    • 2
    Email author
  • Gouenou Coatrieux
    • 1
    • 2
  • Emmanuelle Genin
    • 2
  • Michel Cozic
    • 3
  1. 1.IMT AtlantiquePlouzaneFrance
  2. 2.Unit INSERM 1101 LatimBrest CedexFrance
  3. 3.MED.e.COMPlougastel DaoulasFrance

Personalised recommendations